Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions

Agricultural and Forest Meteorology

Volumn 216, Issue , 2016, Pages 157-169

  Park, Seonyoung ^a   Im, Jungho ^a   Jang, Eunna ^a   Rhee, Jinyoung ^b  

^a Ulsan National Institute of Science and Technology   (South Korea)

^b APEC Climate Center   (South Korea)

Author keywords

Boosted regression trees; Cubist; Drought monitoring; MODIS; Random forest; TRMM

Indexed keywords

CLIMATE CHANGE; ENVIRONMENTAL FACTOR; EVAPOTRANSPIRATION; LAND SURFACE; MACHINE LEARNING; MODIS; NDVI; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; REMOTE SENSING; SENSOR; VISUAL ANALYSIS;

EID: 84945550767     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2015.10.011     Document Type: Article

References (66)

1. Adler R.F., et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol. 2003, 4(6):1147-1167.
2. Allen R., et al. Satellite-based ET estimation in agriculture using SEBAL and METRIC. Hydrol. Process. 2011, 25(26):4011-4027.
3. Alley W.M. The Palmer drought severity index: limitations and assumptions. J. Clim. Appl. Meteorol. 1984, 23(7):1100-1109.
4. Anderson M.C., et al. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J. Clim. 2011, 24(8):2025-2044.
5. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
6. Brown J.F., Wardlow B.D., Tadesse T., Hayes M.J., Reed B.C. The Vegetation Drought Response Index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GIScience Remote Sens. 2008, 45(1):16-46.
7. Cooke W.H., Mostovoy G.V., Anantharaj V.G., Jolly W.M. Wildfire potential mapping over the state of Mississippi: a land surface modeling approach. GIScience Remote Sens. 2012, 49(4):492-509.
8. Edwards D.C. Characteristics of 20th century drought in the United States at multiple time scales 1997, DTIC Document.
9. Elith J., Leathwick J.R., Hastie T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77(4):802-813.
10. Friedman J.H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 2001, 1189-1232.
11. Froeschke J.T., Froeschke B.F. Spatio-temporal predictive model based on environmental factors for juvenile spotted seatrout in Texas estuaries using boosted regression trees. Fish. Res. 2011, 111(3):131-138.
12. Gao B.-C. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58(3):257-266.
13. Gao Z., Wang Q., Cao X., Gao W. The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing. GIScience Remote Sens. 2014, 51(1):1-16.
14. Gessner U., et al. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Global Planet. Change 2013, 110:74-87.
15. Gu Y., Brown J.F., Verdin J.P., Wardlow B. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys. Res. Lett. 2007, 34(6).
16. Handler A. USA corn yields, the El Niñmo and agricultural drought: 1867-1988. Int. J. Climatol. 1990, 10(8):819-828.
17. Hayes M., Svoboda M., Le Comte D., Redmond K.T., Pasteris P. Drought Monitoring: New tools for the 21st Century 2005, Dekker, New York.
18. Heim R.R. A review of twentieth-century drought indices used in the United States. Bull. Am. Meteorol. Soc. 2002, 83(8):1149-1165.
19. Johnson D.M. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 2014, 141:116-128.
20. Keshavarz M.R., Vazifedoust M., Alizadeh A. Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data. Agric. Water Manage. 2014, 132:37-45.
21. Kim Y.H., Im J., Ha H.K., Choi J.-K., Ha S. Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GIScience Remote Sens. 2014, 51(2):158-174.
22. Kim M., Im J., Han H., Kim J., Lee S., Shin M., Kim H. Landfast sea ice monitoring using multisensor fusion in the Antarctic. GIScience Remote Sens. 2015, 52:239-256.
23. Kogan F. Application of vegetation index and brightness temperature for drought detection. Adv. Space Res. 1995, 15(11):91-100.
24. Kuhn M., Weston S., Keefer C., Coulter N. Cubist Models for Regression 2012.
25. Lambert J., Drenou C., Denux J.-P., Balent G., Cheret V. Monitoring forest decline through remote sensing time series analysis. GIScience Remote Sens. 2013, 50(4):437-457.
26. Lawrimore J., Heim R.R., Svoboda M., Swail V., Englehart P.J. Beginning a new era of drought monitoring across North America. Bull. Am. Meteorol. Soc. 2002, 83(8):1191-1192.
27. Li M., Im J., Beier C. Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GIScience Remote Sens. 2013, 50(4):361-384.
28. Li M., Im J., Quackenbush L.J., Liu T. Forest biomass and carbon stock quantification using airborne LiDAR Data: a case study over huntington wildlife forest in the Adirondack Park. IEEE J. Selected Top. Appl. Earth Observ. Remote Sens. 2014, 7(7):3143-3156.
29. Lobell D.B., et al. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 2014, 344(6183):516-519.
30. Lohani V.K., Loganathan G. An early warning system for drought management using the Palmer drought index. J. Am. Water Resour. Assoc. 1997, 33(6):1375-1386.
31. Long J.A., Lawrence R.L., Greenwood M.C., Marshall L., Miller P.R. Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest. GIScience Remote Sens. 2013, 50(4):418-436.
32. Luo L., Wood E.F. Monitoring and predicting the 2007 US drought. Geophys. Res. Lett. 2007, 34(22).
33. Mallya G., Zhao L., Song X., Niyogi D., Govindaraju R. 2012 Midwest Drought in the United States. J. Hydrol. Eng. 2013, 18(7):737-745.
34. Maxell A., Strager M., Warner T., Zegre N., Yuill C. Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation. GIScience Remote Sens. 2014, 51:301-320.
35. McKee T.B., Doesken N.J., Kleist J. Drought monitoring with multiple time scales, Ninth Conference on Applied Climatology 1995, 233-236. American Meteorological Society, Boston.
36. Mizzell H. Improving Drought Detection in the Carolinas: Evaluation of Local, State, and Federal Drought Indicators 2008, University of South Carolina.
37. Narasimhan B., Srinivasan R. Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agric. Forest Meteorol. 2005, 133(1):69-88.
38. Nezlin N., Kostianoy A., Li B.-L. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. J. Arid Environ. 2005, 62(4):677-700.
39. Palmer W.C. Meteorological drought. Research paper No. 45. United States Department of Commerce 1965, Weather Bureau, Washington, DC.
40. Piao S., et al. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. J. Geophys. Res.: Atmospheres (1984-2012) 2003, 108(D14).
41. Prasad A.M., Iverson L.R., Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 2006, 9(2):181-199.
42. Quiring S.M., Ganesh S. Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric. Forest Meteorol. 2010, 150(3):330-339.
43. Quiring S.M., Papakryiakou T.N. An evaluation of agricultural drought indices for the Canadian prairies. Agric. For. Meteorol. 2003, 118(1):49-62.
44. Rhee J., Im J., Carbone G.J. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens. Environ. 2010, 114(12):2875-2887.
45. Rhee J., Im J., Park S. Regional drought monitoring based on multi-sensor remote sensing. Remote Sensing of Water Resources, Disasters and Urban Studies 2015, CRC Press. P.S. Thenkabail (Ed.).
46. Rhee J., Park S., Lu Z. Relationship between land cover patterns and surface temperature in urban areas. GIScience Remote Sens. 2014, 51(5):521-536.
47. Riggins J., Tullis J., Stephen F. Per-segment aboveground forest biomass estimation using LIDAR-derived height percentile statistics. GIScience Remote Sens. 2009, 46(2):232-248.
48. RuleQuest, 2012. http://www.rulequest.com/.
49. Shahabfar A., Eitzinger J. Spatio-temporal analysis of droughts in semi-arid regions by using meteorological drought indices. Atmosphere 2013, 4(2):94-112.
50. Stumpf A., Kerle N. Object-oriented mapping of landslides using random forests. Remote Sens. Environ. 2011, 115(10):2564-2577.
51. Svoboda M., et al. The drought monitor. Bull. Am. Meteorol. Soc. 2002, 83(8):1181-1190.
52. Swain S., Wardlow B.D., Narumalani S., Tadesse T., Callahan K. Assessment of vegetation response to drought in Nebraska using Terra-MODIS land surface temperature and normalized difference vegetation index. GIScience Remote Sens. 2011, 48(3):432-455.
53. Tadesse T., Brown J.F., Hayes M.J. A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the US central plains. ISPRS J. Photogram. Rem. Sens. 2005, 59(4):244-253.
54. Tadesse T., Wardlow B.D., Hayes M.J., Svoboda M.D., Brown J.F. The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness. GIScience Remote Sens. 2010, 47(1):25-52.
55. USDA, 2014. Census of Agriculture State and County Profiles. In: U.S.D.o. Agriculture (Editor).
56. Vicente-Serrano S.M., Beguería S., López-Moreno J.I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 2010, 23(7):1696-1718.
57. Vicente-Serrano S.M., et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. 2013, 110(1):52-57.
58. Wang L., Qu J.J. NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys. Res. Lett. 2007, 34(20).
59. Wardlow B.D., Anderson M.C., Verdin J.P. Remote Sensing of Drought: Innovative Monitoring Approaches 2012, CRC Press.
60. Wilhite D.A. Drought as a natural hazard: concepts and definitions. Drought, a global assessment 2000, 1:3-18.
61. Wilhite D.A., Glantz M.H. Understanding: the drought phenomenon: the role of definitions. Water Int. 1985, 10(3):111-120.
62. Wilhite D.A., Svoboda M.D., Hayes M.J. Understanding the complex impacts of drought: a key to enhancing drought mitigation and preparedness. Water Resour. Manage. 2007, 21(5):763-774.
63. Wu H., Hayes M.J., Weiss A., Hu Q. An evaluation of the Standardized Precipitation Index, the China-Z Index and the statistical Z-Score. Int. J. Climatol. 2001, 21(6):745-758.
64. Yagci A., Di L., Deng M. The effect of corn-soybean rotation on the NDVI-based drought indicators: a case study in Iowa, USA, using Vegetation Condition Index. GIScience Remote Sens. 2015, 52(3):290-314.
65. Zhang A., Jia G. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens. Environ. 2013, 134:12-23.
66. Zhang T., Simelton E., Huang Y., Shi Y. A Bayesian assessment of the current irrigation water supplies capacity under projected droughts for the 2030s in China. Agric. For. Meteorol. 2013, 178:56-65.